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ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis by Wang, Wei-Yao; Huang, Yung-Chang; Ik, Tsi-Ui; Peng, Wen-Chih is a scholarly article available to read on EtoBox.

What is ShuttleSet: A Human-Annotated Stroke-Level Singles Dataset for Badminton Tactical Analysis about?

With the recent progress in sports analytics, deep learning approaches have demonstrated the effectiveness of mining insights into players' tactics for improving performance quality and fan engagement. This is attributed to the availability of public ground-truth datasets. While there are a few available datasets for turn-based sports for action detection, these datasets severely lack structured source data and stroke-level records since these require high-cost labeling efforts from domain experts and are hard to detect using automatic techniques. Consequently, the development of artificial intelligence approaches is significantly hindered when existing models are applied to more challenging structured turn-based sequences. In this paper, we present ShuttleSet, the largest publicly-available badminton singles dataset with annotated stroke-level records. It contains 104 sets, 3,685 rallies, and 36,492 strokes in 44 matches between 2018 and 2021 with 27 top-ranking men's singles and women's singles players. ShuttleSet is manually annotated with a computer-aided labeling tool to increase the labeling efficiency and effectiveness of selecting the shot type with a choice of 18 distinct

Author

Wang, Wei-Yao; Huang, Yung-Chang; Ik, Tsi-Ui; Peng, Wen-Chih

Published

2023

Language

EN